library(tidyverse)
data(mtcars)

We can include R code inline as well like this: The average amount of cylinders of the cars in the mtcars dataset is 6.1875.

Nice Tables

Now let’s create some better looking tables. The following looks good in RStudio, but after rendering not that much.

head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

knitr::kable() creates an actual table in the target format (In RStudio it doesn’t lool that nice though!)

library(knitr)

kable(head(mtcars))
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

The kableExtra package allows us to manipulate the output tables in detail:

library(kableExtra)

kable(head(mtcars)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, angle = -45)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
mtcars %>% 
  count(cyl) %>% 
  mutate(percent = n / sum(n) * 100) %>% 
  kable()
cyl n percent
4 11 34.375
6 7 21.875
8 14 43.750

Regression Tables

mod1 <- lm(mpg ~ cyl + vs + gear, data = mtcars)
mod2 <- lm(mpg ~ cyl + vs + gear + hp + disp, data = mtcars)

with sjPlot:

library(sjPlot)
tab_model(mod1, mod2)
  mpg mpg
Predictors Estimates CI p Estimates CI p
(Intercept) 35.93 20.78 – 51.08 <0.001 28.02 10.62 – 45.41 0.004
cyl -2.87 -4.21 – -1.52 <0.001 -0.92 -2.97 – 1.12 0.385
vs -0.47 -4.71 – 3.77 0.830 -0.20 -4.22 – 3.83 0.924
gear 0.57 -1.38 – 2.52 0.571 1.38 -1.21 – 3.97 0.307
hp -0.03 -0.07 – 0.01 0.150
disp -0.01 -0.04 – 0.01 0.333
Observations 32 32
R2 / R2 adjusted 0.731 / 0.703 0.779 / 0.737

with stargazer:

library(stargazer)
stargazer(mod1, mod2)
% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu % Date and time: Wed, Dec 04, 2019 - 01:48:08

Awesome Tables

formattable

library(formattable)
formattable(mtcars,
            list(mpg = color_tile("yellow", "orange"),
                 area(col = disp) ~ normalize_bar("pink", 0.2),
                 area(col = hp) ~ normalize_bar("lightgreen", 0.2),
                 vs = formatter("span",
    style = x ~ style(color = ifelse(x, "green", "red")),
    x ~ icontext(ifelse(x, "ok", "remove"), ifelse(x, "Yes", "No")))))
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 No 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 No 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 Yes 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 Yes 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 No 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 Yes 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 No 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 Yes 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 Yes 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 Yes 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 Yes 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 No 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 No 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 No 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 No 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 No 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 No 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 Yes 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 Yes 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 Yes 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 Yes 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 No 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 No 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 No 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 No 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 Yes 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 No 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 Yes 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 No 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 No 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 No 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 Yes 1 4 2

reactable

library(reactable)

reactable(mtcars)
reactable(mtcars, filterable = TRUE, searchable = TRUE,
            columns = list(
    mpg = colDef(footer = "mean"),
    cyl = colDef(footer = function(values) mean(values))
  ))

Inserting Citations and a Bibliography

The European languages are members of the same family (Hsiao 2016).

As Draz (2016, 22–47) shows: their separate existence is a myth.

Interactive Elements

visNetwork

library(visNetwork)
library(igraph)
erdos.renyi.game(22, 0.3) %>% 
visIgraph()

htmlwidgets

library(ggplot2)
library(plotly)
p <- ggplot(data = diamonds, aes(x = cut, fill = clarity)) +
            geom_bar(position = "dodge")
ggplotly(p)

References

Draz, Amr, Slim Abdennadher, and Yomna Abdelrahman. 2016. “Kodr: A Customizable Learning Platform for Computer Science Education.” In Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, Ec-Tel 2016, Lyon, France, September 13-16, 2016, Proceedings, edited by Katrien Verbert, Mike Sharples, and Tomaž Klobučar, 579–82. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-45153-4_67.

Hsiao, I-Han. 2016. “Mobile Grading Paper-Based Programming Exams: Automatic Semantic Partial Credit Assignment Approach.” In Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, Ec-Tel 2016, Lyon, France, September 13-16, 2016, Proceedings, edited by Katrien Verbert, Mike Sharples, and Tomaž Klobučar, 110–23. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-45153-4_9.